Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
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Adversarial Generation sentence-pairs (SWAG) 52

Amazon Web Services (AWS) 169

associative neutral networks 3


benchmark tasks, SuperGLUE

BoolQ 114

Commitment Bank (CB) 114

defining 113

Multi-Sentence Reading Comprehension (MultiRC) 115, 116

Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) 116, 117

Recognizing Textual Entailment (RTE) 118

Winograd Schema Challenge (WSC) 118, 119

Words in Context (WiC) 118

BERT-based model

used, for performing SRL experiments 249

BERT-base multilingual model 307, 308

BERT model

fine-tuning 50, 52

pretraining 50, 52

BERT model, fine-tuning 53

attention masks, creating 59

batch size, selecting 60, 61

BERT tokenizer, activating 57

BERT tokens, creating 57

configuration, initializing 61, 62

CUDA, specifying 55

data, converting into torch sensors 60

data, processing 58

dataset, loading 55, 56, 57